def load_checkpoint(self):
# Restart training (useful with oar idempotant)
params = self.params
modelname = params['modelname']
iterators_state = {}
history = {}
if osp.exists(osp.join(modelname, 'model.pth')):
self.warn('Picking up where we left')
# load model's weights
saved_state = torch.load(osp.join(modelname, 'model.pth'))
saved = list(saved_state)
required_state = self.model.state_dict()
required = list(required_state)
del required_state
if "module" in required[0] and "module" not in saved[0]:
for k in saved:
kbis = "module.%s" % k
saved_state[kbis] = saved_state[k]
del saved_state[k]
for k in saved:
if "increment" in k:
del saved_state[k]
self.model.load_state_dict(saved_state)
# load the optimizer's last state:
self.optimizer.load(
torch.load(osp.join(modelname, 'optimizer.pth')
))
history = pl(osp.join(modelname, 'trackers.pkl'))
iterators_state = {'batch_offset': history['batch_offset'],
'epoch': history['epoch']}
elif params['start_from']:
start_from = params['start_from']
# Start from a pre-trained model:
self.warn('Starting from %s' % start_from)
if params['start_from_best']:
flag = '-best'
self.warn('Starting from the best saved model')
else:
flag = ''
# load model's weights
saved_weights = torch.load(osp.join(start_from, 'model%s.pth' % flag))
if params['reverse']:
# adapat the loaded weight for the reverse task
saved_weights = self.reverse_weights(saved_weights)
self.model.load_state_dict(saved_weights)
del saved_weights
# load the optimizer's last state:
if not params['optim']['reset']:
self.optimizer.load(
torch.load(osp.join(start_from, 'optimizer%s.pth' % flag)
))
history = pl(osp.join(start_from, 'trackers%s.pkl' % flag))
self.trackers.update(history)
self.epoch = self.trackers['epoch']
self.iteration = self.trackers['iteration']
# start with eval:
# self.iteration -= 1
return iterators_state
def __init__(self, job_name, params):
super().__init__()
self.version = "tok"
self.logger = logging.getLogger(job_name)
self.margin_sim = params['margin_sim']
self.normalize_batch = params['normalize_batch']
self.penalize_confidence = params['penalize_confidence']
if self.margin_sim:
self.logger.warn('Clipping similarities below %.2f' %
self.margin_sim)
self.limited = params['limited_vocab_sim']
self.alpha = params['alpha_word']
self.tau_word = params['tau_word']
# Load the similarity matrix:
M = pl(params['similarity_matrix'])
self.dense = isinstance(M, np.ndarray)
self.rare = params['promote_rarity']
if self.dense:
M = M - 1 # = -D_ij
if self.rare:
IDF = pl(params['rarity_matrix'])
M -= self.tau_word * self.rare * IDF
del IDF
M = M.astype(np.float32)
M = Variable(torch.from_numpy(M)).cuda()
self.Sim_Matrix = M
n, d = self.Sim_Matrix.size()
else:
if self.rare:
IDF = pl(params['rarity_matrix'])
self.IDF = sparse_torch(IDF).cuda()
del IDF
self.Sim_Matrix = sparse_torch(M).cuda()
n, d = self.Sim_Matrix.size()
del M
def load_checkpoint(self):
# Restart training (useful with oar idempotant)
params = self.params
modelname = params['modelname']
iterators_state = {}
history = {}
print('Checking up', osp.join(modelname, "model.pth"))
if osp.exists(osp.join(modelname, 'model.pth')):
self.warn('Picking up where we left')
# load model's weights
saved_state = torch.load(osp.join(modelname, 'model.pth'))
check = list(saved_state)
for k in check:
if "increment" in k:
del saved_state[k]
self.model.load_state_dict(saved_state)
# load the optimizer's last state:
self.optimizer.load(
torch.load(osp.join(modelname, 'optimizer.pth')
))
history = pl(osp.join(modelname, 'trackers.pkl'))
print('Loaded history:', list(history))
iterators_state = {'src_iterators': history['src_iterators'],
'trg_iterators': history['trg_iterators']}
elif params['start_from']:
start_from = params['start_from']
# Start from a pre-trained model:
self.warn('Starting from %s' % start_from)
if params['start_from_best']:
flag = '-best'
self.warn('Starting from the best saved model')
else:
flag = ''
# load model's weights
saved_weights = torch.load(osp.join(start_from, 'model%s.pth' % flag))
if params['reverse']:
# adapat the loaded weight for the reverse task
saved_weights = self.reverse_weights(saved_weights)
self.model.load_state_dict(saved_weights)
# load the optimizer's last state:
if not params['optim']['reset']:
self.optimizer.load(
torch.load(osp.join(start_from, 'optimizer%s.pth' % flag)
))
history = pl(osp.join(start_from, 'trackers%s.pkl' % flag))
self.trackers.update(history)
self.epoch = self.trackers['epoch']
self.iteration = self.trackers['iteration']
# start with eval:
# self.iteration -= 1
return iterators_state